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In this talk, Kevin will explore the idea of Big Data DumbOps -- not "dumb" because standing up a Big Data stack is easy, but "dumb" because it should be. Few people give much thought to apt-get install foo. Why can’t ’foo’ be a Big Data analytics stack, complete with ingestion, processing, and visualization components? The hard part with solving Big Data problems is in the deployment and configuration of all the services that need to work together (NameNodes, DataNodes, ResourceManagers, oh my). Wouldn’t it be great if there was an easy way to model a Big Data platform, stand that up in a cloud, and get down to business? "Yes" is the right answer, and Juju does just that. Kevin will cover some of the Big Data services available in the Juju ecosystem (Hadoop, Spark, Kafka, etc) and then show how easily these can be deployed to make way for the real fun -- solving Big Data problems.

During his tenure at IBM, Kevin’s projects covered a wide range of development, from tiny embedded operating systems to Linux enablement of the POWER8 hardware platform. Kevin moved to Canonical in 2014 with his focus set on modeling workload deployments at scale. He found his niche on the Juju Big Data team, where his mission is to wrangle the cumbersome install and configuration of Big Data services (Hadoop, Spark, Kafka, etc) into real world... Read More →